Colombo noticeably became the most economical gateway to the Indian subcontinent, in terms of cost as well as time. The Colombo Port Expansion Project (CPEP) started commencement with the purpose of accommodating mega ships, under the long-term strategies of making Colombo the hub of South Asia. In this context, the purpose of this study is to investigate the causal relationship between Indian ports’ originated container traffic, and total transshipments of the port of Colombo, and also to identify the nature of the causality between the two variables, evaluating Granger causality test results. It finds unidirectional causality from total transshipments of Colombo to Indian ports’ originated transshipments in the port of Colombo. It suggested that ongoing port expansion projects, opening up for new markets and attracting new shipping lines in the port of Colombo, have generated significant impact on Indian ports’ container traffic, via the port of Colombo. Findings would be valuable for future forecasting of container traffic in Colombo port and the policy-making process in the port as well.

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology – We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results – The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions – The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.